Month: September 2026

Beautiful dashboards don’t guarantee adoption. David Ohnstad discovered his company’s C-suite was making AI budget decisions in a spreadsheet—bypassing the expensive governance platform entirely. The real problem isn’t technology; it’s alignment between what executives need and what platforms deliver.

Vendors promise unified AI governance, but consolidation actually compounds compliance risks. David Ohnstad breaks down why platform lock-in strategies fail to solve the real governance problems enterprises face with machine learning deployments.

A $4.2M AI platform delivered on time with 94% accuracy. Yet eighteen months later, teams still use Excel in parallel. The real barrier to AI adoption isn’t technology—it’s organizational structure and change management that budgets never address.

A $2.1M AI platform launched successfully with 95% accuracy—yet only 4% of intended users engaged. David Ohnstad explores the organizational readiness gap that determines whether enterprise AI initiatives succeed or stall after deployment.

Three months after deployment, Fortune 500 teams can’t answer basic questions about their AI systems. David Ohnstad explains the fundamental mismatch: vendors design platforms for teams with skills that rarely exist in legacy enterprise environments.

ServiceNow’s latest AI platform promised to manage everything—until real production hit. David Ohnstad explores why proof-of-concepts succeed while deployments fail, and the unglamorous data work required to make enterprise AI actually work.